The complex Ancient Egyptian (AE) writing system was characterised by
widespread use of graphemic classifiers (determinatives): silent (unpronounced)
hieroglyphic signs clarifying the meaning or indicating the pronunciation of
the host word. The study of classifiers has intensified in recent years with
the launch and quick growth of the iClassifier project, a web-based platform
for annotation and analysis of classifiers in ancient and modern languages.
Thanks to the data contributed by the project participants, it is now possible
to formulate the identification of classifiers in AE texts as an NLP task. In
this paper, we make first steps towards solving this task by implementing a
series of sequence-labelling neural models, which achieve promising performance
despite the modest amount of training data. We discuss tokenisation and
operationalisation issues arising from tackling AE texts and contrast our
approach with frequency-based baselines.

通过实施序列标记神经模型，本文首次尝试解决 Ancient Egyptian 文本中分类符号的辨识问题，并对比了以频率为基础的基准模型，取得了令人满意的性能。